LiMT: A Multi-task Liver Image Benchmark Dataset

TL;DR

LiMT is a comprehensive multi-task liver image dataset designed to facilitate simultaneous liver and tumor segmentation, lesion classification, and detection, supporting advanced CAD development and exploring task correlations.

Contribution

This paper introduces LiMT, a multi-task liver dataset with detailed annotations, enabling joint learning across multiple liver-related imaging tasks, which is a novel resource in medical imaging.

Findings

Baseline experimental results provided

Reviewed existing datasets and methods

Dataset includes 150 annotated CT cases

Abstract

Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent years, the application scope of existing datasets remains relatively limited, typically supporting only single tasks, which has somewhat constrained the development of CAD technology. To address the above limitation, in this paper, we construct a multi-task liver dataset (LiMT) used for liver and tumor segmentation, multi-label lesion classification, and lesion detection based on arterial phase-enhanced computed tomography (CT), potentially providing an exploratory solution that is able to explore the correlation between tasks and does not need to worry about the heterogeneity between task-specific datasets during training. The dataset includes CT volumes from 150 different cases, comprising four types of liver diseases as well as normal cases. Each volume has been carefully annotated and calibrated by experienced clinicians. This public multi-task dataset may become a valuable resource for the medical imaging research community in the future. In addition, this paper not only provides relevant baseline experimental results but also reviews existing datasets and methods related to liver-related tasks. Our dataset is available at https://drive.google.com/drive/folders/1l9HRK13uaOQTNShf5pwgSz3OTanWjkag?usp=sharing.